Environmental disturbances such as wind coupled with improper camera mounting may give rise to shaky video in the case of video cameras mounted in outdoor environments. Improper camera mounting may also give rise to shaky video even in an indoor environment. The camera shake results in frame-to-frame distortion, image blurring, or a combination of both depending on the frequency of the disturbance. Camera shake (herein also referred to as “jitter”) is disturbing for human viewer, reduces compression efficiency, and may result in video unsuitable for video analytics, such as motion detection and tracking. Hence, there is a need to reduce camera shake in order to reduce the burden on the operator and improve the reliability of video analytics by reducing the number of false identifications. Reduced jitter also results in lower bit rates for compressed video. Consequently, less demand is placed on the computer network bandwidth when the video is transmitted, or on storage space requirements. Hence, camera stabilization is an important front-end feature of any surveillance system in order to achieve better overall performance.
Camera stabilization can be carried out by sensing the disturbance using active sensors, such as accelerometers or gyroscopes, and applying corrections either in hardware or software based approaches. The performance of those systems is limited by the intrinsic sensitivity of the sensors. This approach is typically taken for camcorder image stabilization, and is suited to the low frequency motion associated with handheld cameras. An advantage of systems based on accelerometers is the ability to compensate for camera jitter (or instability) even in featureless images under low illumination conditions, and are not influenced by objects moving in the field of view.
However, the disturbance-sensing approach is less effective for eliminating high frequency motion associated with the vibrations experienced by mounted cameras used in video surveillance and security. Such vibrations may be induced by wind or traffic, for example.
The surveillance field demands a robust technique for computation of homographies to deal with dynamic scene changes associated with moving objects. Consequently, a stabilization system for moving and Pan-Tilt-Zoom (PTZ) cameras needs to differentiate between intentional camera motions and unintentional camera movements.